Limited mass-independent individual variation in resting metabolic rate in a wild population of snow voles (Chionomys nivalis)
Hagmayer, Andres et al. (2020), Limited mass-independent individual variation in resting metabolic rate in a wild population of snow voles (Chionomys nivalis), Dryad, Dataset, https://doi.org/10.5061/dryad.w3r2280m9
Resting metabolic rate (RMR) is a potentially important axis of physiological adaptation to the thermal environment. However, our understanding of the causes and consequences of individual variation in RMR in the wild is hampered by a lack of data, as well as analytical challenges. RMR measurements in the wild are generally characterized by large measurement errors and a strong dependency on mass. The latter is problematic when assessing the ability of RMR to evolve independently of mass. Mixed models provide a powerful and flexible tool to tackle these challenges, but they have rarely been used to estimate repeatability of mass-independent RMR from field data. We used respirometry to obtain repeated measurements of RMR in a long-term study population of snow voles (Chionomys nivalis) inhabiting an environment subject to large circadian and seasonal fluctuations in temperature. Using both uni- and bivariate mixed models, we quantify individual repeatability in RMR and decompose repeatability into mass-dependent and mass-independent components, while accounting for measurement error. RMR varies among individuals, i.e. is repeatable (R=0.46), and strongly co-varies with BM. Indeed, much of the repeatability of RMR is attributable to individual variation in BM, and the repeatability of mass-independent RMR is reduced by 41% to R=0.27. These empirical results suggest that the evolutionary potential of RMR independent of mass may be severely constrained. This study illustrates how to leverage bivariate mixed models to model field data for metabolic traits, correct for measurement error, and decompose the relative importance of mass-dependent and mass-independent physiological variation.
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Award: 31003A_141110